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WO2002080148A1 - Dispositif eliminateur de bruit - Google Patents

Dispositif eliminateur de bruit Download PDF

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Publication number
WO2002080148A1
WO2002080148A1 PCT/JP2001/002596 JP0102596W WO02080148A1 WO 2002080148 A1 WO2002080148 A1 WO 2002080148A1 JP 0102596 W JP0102596 W JP 0102596W WO 02080148 A1 WO02080148 A1 WO 02080148A1
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WO
WIPO (PCT)
Prior art keywords
noise
spectrum
signal
band
calculated
Prior art date
Application number
PCT/JP2001/002596
Other languages
English (en)
Japanese (ja)
Inventor
Satoru Furuta
Shinya Takahashi
Original Assignee
Mitsubishi Denki Kabushiki Kaisha
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority to US10/276,292 priority Critical patent/US7349841B2/en
Application filed by Mitsubishi Denki Kabushiki Kaisha filed Critical Mitsubishi Denki Kabushiki Kaisha
Priority to JP2002578288A priority patent/JP3574123B2/ja
Priority to CNB018101143A priority patent/CN1282155C/zh
Priority to EP10006260.3A priority patent/EP2242049B1/fr
Priority to EP01917568A priority patent/EP1376539B8/fr
Priority to PCT/JP2001/002596 priority patent/WO2002080148A1/fr
Priority to EP10006261.1A priority patent/EP2239733B1/fr
Priority to DE60142800T priority patent/DE60142800D1/de
Publication of WO2002080148A1 publication Critical patent/WO2002080148A1/fr
Priority to US11/927,354 priority patent/US8412520B2/en
Priority to US11/927,415 priority patent/US7660714B2/en
Priority to US11/927,509 priority patent/US20080056510A1/en
Priority to US11/927,478 priority patent/US7788093B2/en

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Classifications

    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering

Definitions

  • the present invention relates to a noise suppression device for suppressing noise other than, for example, a speech signal in a speech communication system, a speech recognition system, and the like used in various noise environments.
  • a noise suppressor for suppressing an unintended signal such as noise superimposed on an audio signal is disclosed in, for example, Japanese Patent Application Laid-Open No. 7-36695. This is because noise on the amplitude spectrum shown in the literature, Steven F. Boll, "Suppression of Acoustic noise in speech using spectral subtraction", IEEE Trans. ASSP, Vol. ASSP-27, No. 2, April 1979. Suppression is based on the so-called Spectral Subtraction (SS) method.
  • SS Spectral Subtraction
  • FIG. 1 is a block diagram showing a configuration of a conventional noise suppression device disclosed in the above publication.
  • 1 1 1 is an input terminal
  • 1 1 2 is a frame processing '' windowing processing circuit
  • 1 1 3 is 1 circuit
  • 1 1 4 is a band division circuit
  • 1 1 5 is a noise estimation circuit
  • 1 1 6 is a Speech estimation circuit
  • 1 17 is Pr (S p) calculation circuit
  • 1 18 is Pr (S p IY) calculation circuit
  • 1 19 is maximum likelihood filter
  • 1 20 is soft decision suppression circuit
  • 1 2 1 is a filter processing circuit
  • 1 2 2 is a band conversion circuit
  • 1 2 3 is a spectrum correction circuit
  • 1 2 4 is an IFFT circuit
  • 1 2 5 is an overlap addition circuit
  • 1 2 6 is an output terminal. is there.
  • FIG. 2 shows the configuration of the noise estimation circuit 115 in the conventional noise suppression device.
  • 115A is an RMS calculation circuit
  • 115B is a relative energy calculation circuit
  • 115C is a minimum RMS calculation circuit
  • 115D is a maximum signal calculation circuit.
  • the input terminal 111 receives an input signal y [t] including a voice component and a noise component.
  • This input signal y [t] is a digital signal of, for example, a sampling frequency; FS, and is sent to a framing / windowing processing circuit 112 to have a frame length of FL samples, for example, a frame of 160 samples.
  • the window is divided and windowing processing is performed before the next FFT processing.
  • the FFT circuit 113 the FFT (Fast Fourier Transform) processing of 256 points is performed, and the obtained frequency spectrum amplitude value is subjected to, for example, a pan division circuit 114. Divided into 18 bands.
  • the noise estimation circuit 115 distinguishes the noise in the input signal y [t] from speech and detects a frame estimated to be noise.
  • the operation of the noise estimation circuit 115 will be described with reference to FIG.
  • the input signal y [t] is sent to an RMS (Root Mean Square: root mean square) calculation circuit 1 15 A, and the short-time RMS value for each frame is calculated.
  • the short-term RMS value is sent to the relative energy calculation circuit 115B minimum: RMS calculation circuit 115C, maximum signal calculation circuit 115D and noise spectrum estimation circuit 115E.
  • the noise spectrum estimating circuit 115E has the relative energy calculating circuit 115B, the minimum RMS calculating circuit 115C, and the output from the maximum signal calculating circuit 115D.
  • the output from the band dividing circuit 114 is sent.
  • the RMS calculation circuit 115A calculates the RMS value RMS [k] of the signal for each frame according to the following equation (1). Also, the relative energy calculation circuit 1 At 15 B, the relative energy of the current frame, d B — re 1 [k], relative to the decay energy from the previous frame (decay time 0.65 seconds) is calculated.
  • E-dec [k] max (E [k], exp (-FL / 0.65 * FS) E _ dec [k _ 1]) (1)
  • the minimum RMS calculation circuit 1 15 C evaluates the background noise level To do this, calculate the minimum noise RMS value of the current frame, MinInoise—short, and the long-term minimum noise RMS value, MinInoise_long, updated every 0.6 seconds. Note that the long-term minimum noise RMS value MinNoise-long is used instead when the minimum noise RMS value MinNoisse-short of the current frame cannot follow a sudden change in the noise level.
  • the long-term maximum signal RMS value MaxSignal_long is used instead when the maximum signal RMS value of the current frame cannot follow a sudden change in signal level.
  • the short-term maximum signal RM S value M a XS igna 1—short and short-term minimum noise RMS value M in Noise_s hort
  • the maximum SNR value Max SNR of the current frame signal is estimated. .
  • a normalized parameter NR—leveel indicating a relative noise level in a range from 0 to 1 is calculated.
  • the noise spectrum estimating circuit 115E the relative energy calculating circuit 115B, the minimum RMS calculating circuit 115C and the maximum signal calculating circuit 115D Using the value calculated in step (1), it is determined whether the state of the current frame is a speech signal or noise. If the current frame is determined to be noise, the time-averaged estimate N [w, k] of the noise spectrum is updated by the signal spectrum Y [w, k] of the current frame. w indicates the band number of the band division. The speech estimation circuit 1 16 in FIG. 1 calculates the SN ratio for each band w divided by the band.
  • the speech spectrum is roughly estimated on the assumption that no noise exists (clean conditions), and the speech spectrum rough estimate S, [w, k] Ask for.
  • the rough estimate S, [w, k] of the speech spectrum is used to calculate a probability P r (S p IY) described later.
  • the speech estimation circuit 1 16 calculates the above-mentioned speech spectrum rough estimation value S 5 [w, k] and the speech spectrum estimation value S [w, k ⁇ 1] one frame before. Is used to calculate the speech spectrum estimate S [w, k] of the current frame. Using the obtained speech spectrum estimation value S [w, k] and the noise spectrum estimation value N [w, k] output from the noise spectrum estimation circuit 115E, the following equation is obtained. Calculate the SN ratio SNR [w, k] of subband ⁇ according to (3).
  • the speech estimation circuit 1 16 uses the above-mentioned SN ratio SNR [w, kl] for each subband to adjust the noise
  • the SN ratio SNR—new [w, k] is calculated by the following equation (4).
  • MIN—SNR () is a function that determines the minimum value of SNR—new [w, k]
  • the argument snr is synonymous with the subband SN ratio SNR [w, k]. is there.
  • the above-obtained SNH__new [w, k] is the instantaneous subband SN ratio in the current frame with its minimum value restricted.
  • This SNR—new [w, k] is, for example, for a signal having a high SN ratio as a whole such as a sound part, the minimum value taken by the subband SN ratio is 1.5 (dB). Can be dropped. Also, for a signal having a low instantaneous SN ratio, such as a noise portion, the minimum value taken by the subband SN ratio does not become smaller than 3 (dB).
  • the P r (S p) calculation circuit 1 17 calculates the probability P r (S p) of the presence of the speech signal in the assumed input signal, that is, under a clean condition. This probability Pr (Sp) is calculated by using the NR_1 eve 1 function calculated by the maximum signal calculation circuit 115D.
  • the P r (S p IY) calculation circuit 118 calculates the probability P r (S p IY) of the presence of the audio signal in the input signal y [t] in which noise is actually mixed.
  • the probability P r (S p IY) is calculated from the probability P r (S p) output from the Pr (S p) calculation circuit 117 and the subband SNR S NR calculated by the above equation (4). — Calculated using n ew [w, k].
  • Y) [w, k] have the meaning of the subband w of the spectrum amplitude signal Y [w, k].
  • the noise signal N is removed from the spectrum amplitude signal Y by the following equation (5), and the noise spectrum removed signal H [w, k] is output.
  • Y) calculation circuit 1 18 Using the probability P r (HIIY) [w, k] of the output of the noise spectrum removal signal H [w, k] according to the following equation (6), the spectrum of each sub-band w of the noise spectrum elimination signal H [w, k] is obtained. It performs vector amplitude suppression and outputs the spectrum suppression signal H s [w, k].
  • H s [w, k] P r (HI
  • the filter processing circuit 121 smoothes the spectrum suppression signal H s [w, k] output from the soft decision suppression circuit 120 in the frequency axis direction and the time axis direction. To reduce the sense of discontinuity in the spectrum suppression signal H s [w, k].
  • the node conversion circuit 122 performs band expansion conversion on the smoothed signal output from the filter processing circuit 121 by interpolation.
  • the spectrum correction circuit 123 adds the band to the imaginary part of the FFT coefficient of the input signal obtained by the FFT circuit 113 and the real part of the FFT coefficient obtained by the band conversion circuit 122. Multiply the output signal of the divider circuit 14 to correct the spectrum.
  • the IFFT circuit 124 performs an inverse FFT process using the signal obtained by the spectrum correction circuit 123.
  • the overlap addition circuit 125 the IFFT output signal for each frame is superimposed on the frame boundary, and the noise-reduced output signal is output from the output terminal 126.
  • the noise suppression device of the above has a configuration in which the noise suppression amount can be adjusted according to the subband SN ratio even if the noise and voice level of the input signal fluctuates. For signals that have a low signal-to-noise ratio, the minimum value of each subband signal-to-noise ratio can be reduced, and the amount of amplitude suppression can be reduced for subbands with low signal-to-noise ratios. Can be prevented.
  • the minimum value of each sub-band SN ratio is increased, and sufficient amplitude suppression is performed for sub-bands with a low SN ratio. The generation of feeling is suppressed.
  • the conventional noise suppression device is configured as described above, In order to prevent residual noise from occurring, the noise should be suppressed with a constant noise suppression characteristic in the frequency direction in all bands, but the estimated noise spectrum is the average noise noise in the past. Therefore, the actual noise spectrum in the current frame does not match the shape of the spectrum, resulting in an estimation error of the subband SNR. There is a problem that noise suppression cannot be performed with the noise suppression amount characteristic.
  • the present invention has been made to solve the above-described problems, and suppresses the generation of residual noise in a noise frame by a simple method.
  • the aim is to obtain a suppression device. Disclosure of the invention
  • the noise suppression device includes: a time / frequency conversion unit that performs frequency analysis of an input signal for each frame to convert the input signal into a phase spectrum and an input signal spectrum; and whether the frame of the input signal is noise.
  • a noise-likeness analysis means for calculating a noise-likeness signal, which is an indicator of the presence of sound, and an input signal spectrum converted by the time / frequency conversion means, and an input signal average spectrum for each small band.
  • a vector is calculated based on the calculated average signal spectrum of the input signal for each small band and the noise-likeness signal calculated by the noise-likeness analysis means.
  • Noise spectrum estimating means for updating the estimated noise spectrum for each small band estimated from the past frame, the noise likeness signal calculated by the above noise likeness analyzing means, and the time-frequency conversion
  • the input signal spectrum converted by the means and the estimated noise spectrum for each small band updated by the noise spectrum estimating means are input. Is calculated based on the input noise likelihood signal, and the mixing ratio of the estimated noise spectrum for each input sub-band and the calculated input signal average spectrum for each sub-band is calculated.
  • the sub-pan which calculates the SN ratio for each sub-band based on the input estimated noise spectrum for each sub-band, the averaged input signal spectrum for each sub-band calculated, and the calculated mixing ratio De SN ratio calculation Using the SN ratio for each sub-band calculated by the sub-band SN ratio calculating means, for each sub-band with respect to the estimated noise spectrum for each sub-band updated by the noise spectrum estimating means.
  • the spectrum suppression amount calculating means for calculating the spectrum suppression amount of the subband and the spectrum suppression amount for each small band calculated by the spectrum suppression amount calculation means Using the spectrum suppression amount calculating means for calculating the spectrum suppression amount of the subband and the spectrum suppression amount for each small band calculated by the spectrum suppression amount calculation means.
  • the spectrum amplitude of the input signal spectrum converted by the frequency conversion means is suppressed, and the noise suppression spectrum is output and the spectrum suppression means outputs the spectrum.
  • Frequency / time conversion means for converting the noise removal spectrum into a time-domain noise suppression signal using the phase spectrum converted by the time /
  • the mixing ratio calculated by the sub-band SN ratio calculating means is determined by a function proportional to the noise likeness signal. It is.
  • the noise suppression device is characterized in that the mixing ratio calculated by the subband SN ratio calculating means is determined by a function proportional to the noise likeness signal, in which a predetermined threshold value is set such that the lower the higher the higher the band, the lower the threshold is set. Is what is done.
  • the mixing ratio calculated by the subband SN ratio calculating means is weighted so as to increase as the frequency increases.
  • the smoothing can be performed so that the variation of the SN ratio in the high frequency band is further reduced, so that the generation of the residual noise in the high frequency band can be further suppressed.
  • the noise suppression device is configured such that the mixing ratio calculated by the subband SN ratio calculation means is weighted when the noise likeness signal exceeds a predetermined threshold.
  • the mixing ratio calculated by the subband SN ratio calculation means is set by a predetermined value corresponding to the noise likeness signal.
  • minute fluctuations in the mixing ratio in the time direction are absorbed by a predetermined constant value, so that the mixing ratio can be obtained stably, and furthermore, the generation of residual noise can be suppressed.
  • the mixing ratio calculated by the subband SN ratio calculating means is set by a predetermined value for each small band.
  • minute fluctuations in the mixing ratio in the time direction are absorbed by a predetermined constant value, so that the mixing ratio for each small band can be obtained stably, and further generation of residual noise is suppressed. There is an effect that can be.
  • the noise suppression device is weighted such that the mixing ratio for each small band calculated by the subband SN ratio calculating means increases as the frequency increases.
  • the generation of residual noise is further suppressed by the synergistic effect of performing smoothing so as to reduce the SN ratio in a high frequency range. This has the effect of being able to do so.
  • the mixing ratio calculated by the sub-band SN ratio calculation means is weighted when the noise-likeness signal exceeds a predetermined threshold.
  • FIG. 1 is a block diagram showing a configuration of a conventional noise suppression device.
  • FIG. 2 is a professional diagram showing the configuration of a noise estimation circuit in a conventional noise suppression device.
  • FIG. 2 is a professional diagram showing the configuration of a noise estimation circuit in a conventional noise suppression device.
  • FIG. 3 is a block diagram showing a configuration of a noise suppression device according to Embodiment 1 of the present invention.
  • FIG. 4 is a block diagram showing a configuration of a sub-band SN ratio calculating means in the noise suppression device according to Embodiment 1 of the present invention.
  • FIG. 5 is a block diagram showing a configuration of noise likelihood analysis means in the noise suppression device according to Embodiment 1 of the present invention.
  • FIG. 6 is a block diagram showing a configuration of a noise spectrum estimating means in the noise suppression device according to Embodiment 1 of the present invention.
  • FIG. 7 is a block diagram showing a configuration of a spectrum suppression amount calculating means in the noise suppression device according to the first embodiment of the present invention.
  • FIG. 8 is a block diagram showing a configuration of a spectrum suppression means in the noise suppression device according to the first embodiment of the present invention.
  • FIG. 9 is a diagram showing a frequency band division table in the noise suppression device according to Embodiment 1 of the present invention.
  • FIG. 10 is a diagram showing the relationship between the average spectrum of the input signal, the estimated noise spectrum, and the subband SN ratio in the noise suppression device according to Embodiment 1 of the present invention.
  • FIG. 11 is a diagram illustrating an input signal average spectrum, an estimated noise spectrum, and a subband SN in a case where weighting in the frequency direction is performed on the mixing ratio in the noise suppression apparatus according to Embodiment 5 of the present invention. It is a figure showing the relation with a ratio.
  • FIG. 3 is a block diagram showing a configuration of a noise suppression device according to Embodiment 1 of the present invention.
  • 1 is an input signal terminal
  • 2 is a time / frequency conversion means for analyzing the frequency of an input signal for each frame and converting it into an input signal spectrum and a phase spectrum
  • 3 is an input signal
  • the noise-likeness analysis means 4 calculates a noise-likeness signal, which is an index of whether the frame is noise or sound.4.Inputs the input signal spectrum converted by the time / frequency conversion means 2 to reduce the noise.
  • the input signal average spectrum for each band is calculated, and is estimated from past frames based on the calculated input signal average spectrum for each small band and the noise likeness signal calculated by the noise likeness analysis means 3. This is a noise spectrum estimating means for updating the estimated noise spectrum for each small band.
  • reference numeral 5 denotes a noise likeness signal calculated by the noise likeness analyzing means 3, an input signal spectrum converted by the time / frequency converting means 2, and a noise spectrum estimating means.
  • the estimated noise spectrum of the small band updated in step 4 is input, the average spectrum of the input signal for each small band is calculated from the input signal spectrum, and the input signal is calculated based on the input noise-likeness signal.
  • the calculated mixture ratio of the estimated noise spectrum for each sub-band and the calculated average spectrum of the input signal for each sub-band is calculated, and the estimated noise spectrum for each input sub-band and the calculated small noise spectrum are calculated.
  • Sub-band SN ratio calculation means for calculating the SN ratio for each sub-band based on the input signal average spectrum for each band and the calculated mixing ratio, 6 for each sub-band calculated by the sub-band SN ratio calculation means 5
  • the spectral suppression amount calculating means for calculating the spectral suppression amount for each small band with respect to the estimated noise spectrum for each small band updated by the vector estimating means 4, and 7 is the spectral suppression amount.
  • the spectral amplitude of the input signal spectrum converted by the time / frequency converter 2 is reduced by using the amount of spectrum suppression for each small band calculated by the calculating means 6 to remove noise.
  • a spectrum suppression means for outputting a spectrum, and 8 is a spectrum suppression means.
  • Frequency / time conversion means for converting the noise removal spectrum output by the means 7 into a noise suppression signal in the time domain using the phase spectrum converted by the time frequency conversion means 2; Overlap addition means for performing a superposition process on a frame boundary portion of the noise suppression signal converted by the time conversion means 8 and outputting a noise reduction signal subjected to the noise reduction processing.
  • Reference numeral 10 denotes an output signal terminal.
  • FIG. 4 is a block diagram showing a configuration of the subband SN ratio calculating means 5 in the noise suppression device according to Embodiment 1 of the present invention.
  • 5A is a band division filter
  • 5B is a mixing ratio calculation circuit
  • 5C is a subband SN ratio calculation circuit.
  • FIG. 5 is a block diagram showing a configuration of the noise likeness analyzing means 3 in the noise suppression device according to Embodiment 1 of the present invention.
  • 3A is a windowing circuit
  • 3B is a one-pass filter
  • 3C is a linear prediction analysis circuit
  • 3D is an inverse filter
  • 3E is an autocorrelation coefficient calculation circuit
  • 3F is a maximum value.
  • the detection circuit, 3G is a noise-likeness signal calculation circuit.
  • FIG. 6 is a block diagram showing a configuration of the noise vector estimating means 4 in the noise suppression device according to Embodiment 1 of the present invention.
  • 4A is an update rate coefficient calculation circuit
  • 4B is a band division filter
  • 4C is an estimated noise spectrum update circuit.
  • FIG. 7 is a block diagram showing a configuration of the spectrum suppression amount calculating means 6 in the noise suppression device according to the first embodiment of the present invention.
  • 6A is a frame noise energy calculation circuit
  • 6B is a spectrum suppression amount calculation circuit.
  • FIG. 8 is a block diagram showing a configuration of the spectrum suppression means 7 in the noise suppression device according to the first embodiment of the present invention.
  • 7A is an interpolation circuit
  • 7B is a spectrum suppression circuit. Next, the operation will be described.
  • the input signal s [t] is sampled at a predetermined sampling frequency (for example, 8 kHz), is divided into predetermined frame units (for example, 20 ms), and is input from the input signal terminal 1.
  • This input signal s [t] is an audio signal mixed with background noise, or a signal containing only background noise.
  • the time / frequency conversion means 2 converts the input signal s [t] into an input signal spectrum S [f] and a phase spectrum P [f] frame by frame using, for example, a 256-point FFT.
  • FFT is a well-known technique, and a description thereof will be omitted.
  • the sub-band SN ratio calculation means 5 includes an input signal spectrum S [f] output from the time-frequency conversion means 2, a noise-likeness signal Noise-level output from the noise-likeness analysis means 3 described later, and a noise-level signal Using the estimated noise spectrum Na [i], which is the average noise spectrum estimated from the frame determined to be the past noise, output by the noise spectrum estimating means 4 that generates the frequency band of the current frame.
  • the SNR [i] of another SN ratio (hereinafter referred to as a subband SN ratio) is obtained by the following method.
  • FIG. 9 is a diagram showing a frequency band division table in the noise suppression device according to Embodiment 1 of the present invention.
  • a small bandwidth of 19 such that the bandwidth is narrow in the low frequency band and the bandwidth becomes wider in the higher frequency band.
  • the average value of the spectral components belonging to the subband is obtained for each subband i, and the average value is output as the input signal average spectrum Sa [i].
  • Sa [i u — fl [i] + l), i 0, ..., 18 (7)
  • the mixing ratio calculation circuit 5B shown in FIG. 4 inputs a noise-likeness signal Noise-level described later and uses a noise spectrum described later used when calculating a subband SN ratio SNR [i].
  • the mixing ratio m of the estimated noise spectrum Na [i] output from the estimating means 4 and the input signal average spectrum Sa [i] output from the band division filter 5A is calculated.
  • the noise likelihood signal Noise_ 1 eve1 is used as the mixing ratio m
  • the function for determining the mixing ratio m is as shown in equation (8).
  • the input signal average spectrum Sa [i] output from the above-mentioned band division filter 5A and the noise spectrum estimation means 4 are used.
  • the subband SN ratio SNR corresponding to the subband i according to the following equation (9). Calculate [i].
  • the subband SN ratio SNR [i] is calculated using the mixing ratio m, and if the current frame has a large degree of noise, the subband SN ratio SNR [i] is smoothed in the frequency direction. And when the level of noise is small, Can reduce the degree of smoothing in the frequency direction of the subband SN ratio SNR [i]. Therefore, it is possible to control the smoothing in the frequency direction of the sub-band SN ratio S NR [i] according to the noise likelihood of the current frame.
  • FIG. 10 shows an average spectrum of input signal S a [i] (noise spectrum of current frame: solid line) when the current frame is a noise frame in the noise suppression device according to Embodiment 1 of the present invention.
  • FIG. 10 shows an average spectrum of input signal S a [i] (noise spectrum of current frame: solid line) when the current frame is a noise frame in the noise suppression device according to Embodiment 1 of the present invention.
  • FIG. 10 (a) shows a case where the estimated noise spectrum Na [i] is not mixed with the average spectrum S a [i] of the input signal when the sub-band SN ratio SNR [i] is calculated.
  • the obtained subband SN ratio SNR [i] has a shape with large fluctuations in the frequency direction.
  • the subband SN ratio SNR [i] Since the noise spectrum Na [i] can be approximated to the actual noise vector of the current frame, the subband SN ratio SNR [i] has a shape with little fluctuation in the frequency direction. Therefore, an erroneous estimation that makes the sub-band SNR SNR [i] large (or small) in a band containing a high-power spectral component in the noise frame.
  • the subband S / N ratio S NR [i] can be smoothed so as to suppress it.
  • the input signal s [t] is inputted, and the noise likeness signal noisys e_level 1 which is an index of whether the state of the current frame is a noise or a sound is inputted.
  • the calculation is performed by the following method.
  • the input signal s [t] is windowed according to the following equation (10), and the windowed input signal s-w [t] is output.
  • the window function for example, a Haning window H anw in [t 1 use.
  • N is the frame length, and N is assumed to be 160.
  • the windowed input signal s—w [t] output from the windowing circuit 3A is input.
  • the cutoff frequency is 2 kHz.
  • the mouth-pass filter signal s-lpf [t] output from the low-pass filter 3B is input, and linear prediction is performed by a known method such as the Levinson-Durbin method. Calculate and output the coefficient (for example, the 10th order of the parameter) a 1 pha.
  • the mouth-to-pass fill evening signal s_l pf [t] output from the mouth-to-pass fill evening 3B and the linear prediction coefficient a 1 pha output from the linear prediction analysis circuit 3C are input. Performs inverse filtering of the one-pass filter signal s-1 pf [t] and outputs a low-pass linear prediction residual signal res [t].
  • the mouth-path linear prediction residual signal res [t] output by the inverse filter 3D is input, and a mouth-to-pass is calculated according to the following equation (11).
  • An autocorrelation analysis of the linear prediction residual signal res [t] is performed to obtain an Nth-order autocorrelation coefficient ac [k].
  • the autocorrelation coefficient ac [k] output from the autocorrelation coefficient calculation circuit 3E is input, and the positive maximum The value of the autocorrelation coefficient is retrieved and the maximum value of the autocorrelation coefficient A C_max is output.
  • the noise-likeness signal calculation circuit 3G inputs the maximum autocorrelation coefficient A C_max output from the maximum value detection circuit 3F, and sets the noise-likeness signal Noise—level according to the following equation (1 2). Output.
  • the noise spectrum estimating means 4 shown in FIG. 6 inputs the noise likeness signal Noise—level output from the noise likeness analyzing means 3 and inputs the noise likelihood signal N by the following method. After determining the estimated noise spectrum update rate coefficient r corresponding to oise perfumelevel, the estimated noise spectrum Na [i] is updated using the input signal spectrum S [f].
  • the estimated noise spectrum update rate coefficient r used to update the estimated noise spectrum Na [i] is set to 1.0, and the value of the noise-like signal Noise_level is 1.0. It is assumed that the closer to, the higher the probability that the current frame is noise, and the setting is made so as to largely reflect the input signal spectrum S [f] of the current frame. For example, as shown in the following equation (13), the value of the estimated noise spectrum update speed coefficient r is increased as the value of Noise — 1 eve 1 is increased.
  • the input signal spectrum S [f] is input as an average spectrum for each subband.
  • the estimated noise spectrum updating circuit 4C uses the estimated noise spectrum estimated from the past frame according to the following equation (14). Update the torque N a [i].
  • Na_o ld [i] is the estimated noise spectrum before updating and is stored in the memory (not shown) in the noise suppression device.
  • Na [i] is the estimated noise spectrum after updating. This is a noise spectrum.
  • the torque suppression amount calculating means 6 the subband SN ratio SNR [i] output from the subband SN ratio calculating means 5 and the estimated noise spectrum Na [i] output from the noise spectrum estimating means 4 Based on the frame noise energy npow obtained from the above, the spectral suppression amount [i] of subband i ⁇ is obtained by the following method.
  • the estimated noise spectrum N a [i] output from the noise spectrum estimating means 4 is input, and the noise power of the current frame is calculated according to the following equation (15). Calculate the frame noise energy npow npow (1 5)
  • the subband SNR SNR [i] and the frame noise energy npow are input, and the spectrum is calculated according to the following equation (16).
  • min (a, b) is a function that returns the smaller of the two arguments a and b.
  • the spectrum suppression means in Fig. the input signal spectrum S [f] output from the time-Z frequency conversion means 2 and the spectrum suppression amount [i] output from the noise spectrum suppression amount calculation means 6 are input, and the input signal spectrum is input. Suppresses the amplitude of the torque of the torque S [f] and outputs the noise reduction spectrum S r [f].
  • the spectrum suppression amount [i] is input, and the spectrum suppression amount for each subband i is expanded into the spectrum components belonging to each subband, and the spectrum components are: Outputs the spectrum suppression amount aw [f], which is the value for each e.
  • the spectrum suppression circuit 7B suppresses the spectrum amplitude of the input signal spectrum S [f] according to the following equation (17) and outputs the noise removal spectrum Sr [f]. .
  • the frequency / time conversion means 8 takes the inverse procedure of the time / frequency conversion means 2, and performs inverse FFT, for example, to calculate the spectrum. Using the noise removal spectrum S r [f] output from the suppression means 7 and the phase vector P [f] output from the time-Z frequency conversion means 2, a noise suppression signal sr ' Convert to [t] and output.
  • each of the outputs from the frequency / time conversion means 8 The inverse FFT output signal sr for each frame is superimposed on the frame boundary of the sr, [t], and the noise-reduced noise removal signal sr [t] is output from the output signal terminal 10.
  • the spectrum suppression amount [i] is obtained using the sub-band SN ratio S NR [i], which has a small variation in the frequency direction, and the spectrum is calculated using the spectrum suppression amount [i].
  • the subband mixing ratio m calculated by the subband SN ratio calculating means 5 is calculated for each subband i by using, for example, a function of the noise-likeness signal Noise__1eve1. It is also possible to control as the rate m [i].
  • the threshold value N—TH [i] for passing the value of V e 1 low.
  • the subband mixing ratio m [i] in the higher frequency range can be increased, so that the subband SN ratio in the higher frequency range SNR [i] And the deterioration of the estimation accuracy of the high-frequency noise spectrum can be suppressed. As a result, the high-frequency residual noise can be further suppressed.
  • the threshold value N-TH [i] in equation (18) does not need to be prepared for each subband. For example, subbands 0 and 1, subbands 2 and 3,.
  • the threshold value may be shared by two adjacent subbands.
  • functions are prepared for all sub-bands, and the sub-band mixing ratio is individually controlled.
  • the mixing ratio m obtained from the entire frequency band at 1 is output as the subband mixing ratio m [0] to m [9], and the other high-band subband mixing ratios m [10] to m [18] ]
  • the mixing ratio m is set as the sub-band mixing ratio m [i] for each sub-band i, for example, using the function of the noise-likeness signal Noise-1eve1.
  • the mixing ratio m is set to a plurality of predetermined values corresponding to the noise-like signal Noise-level, and the level of the noise-likeness signal Noise-level is high. In this case, it is possible to select a large value, and when the level of the noise-like signal Noise-level is low, it is possible to select a small value.
  • the time in the first embodiment can be reduced.
  • Stable mixing because the fine fluctuation of the mixing ratio m in the time direction is absorbed by a predetermined constant value, compared to the control of the mixing ratio m by the function of the noise-likeness signal Noise_1 eve 1 that fluctuates in the direction. Rate m can be obtained, and the generation of residual noise can be further suppressed. The effect is obtained.
  • the subband mixing ratio m [i] is set with a plurality of predetermined values corresponding to the noise-likeness signal Noise_le_Ve1.
  • the minute fluctuation of the subband mixing ratio m [i] Since it is absorbed by a predetermined constant value, the subband mixing ratio m [i] can be obtained stably, and the effect of suppressing the generation of residual noise can be obtained.
  • the subband mixing ratio m [i] can be weighted in the frequency direction such that the mixing ratio m [i] increases as the frequency becomes higher, for example.
  • the subband mixing ratio m [i ] For example, as shown in the following equation (20), by multiplying the noise-like signal noisys e_leVe1 by a weighting factor w [i] corresponding to the frequency, the subband mixing ratio m [i ].
  • Fig. 11 shows that the mixing ratio m [i] is weighted in the frequency direction under the condition of equation (20). In this example, it can be confirmed that the degree of smoothing of the high-band sub-band SNR [SNR] is enhanced.
  • the frequency sub-band mixing ratio m [i] in the frequency domain is increased so as to increase the high-band subband mixing ratio m [i].
  • the weighting it is possible to smooth the fluctuation of the sub-band SNR SNR [i] in the high frequency band, so that the effect of suppressing the generation of the high frequency residual noise can be further reduced. can get.
  • all subbands are weighted in the frequency direction.
  • only subbands 10 to 18 are assigned to high frequency subbands. May be weighted only o
  • the subband mixing ratio m [i] is weighted even when a predetermined constant is used instead of the function for determining the subband mixing ratio m [i] of the second embodiment.
  • the frequency direction is set so as to increase the high-band subband mixing ratio m [i].
  • the smoothing is performed so as to reduce the subband SN ratio SNH [i] in the high band.
  • the subband mixing ratio m [i] is determined by setting the noise likeness signal N 0 ise — 1 eve 1 of the current frame to a predetermined threshold value m—th If it does not satisfy [i], it is possible not to perform weighting.
  • Equation (22) is an example in which the 0th subband mixing ratio m [0] is weighted.
  • weighting is performed only when the noise likeness signal Noise_leVe1 exceeds a predetermined threshold.
  • the subband SN ratio calculation means 5 prevents unnecessary subband SN ratio smoothing and prevents the SN ratio from being reduced. Therefore, it is possible to prevent the quality of the output sound from deteriorating. You.
  • the subband mixing ratio m [i] is changed to the noise likeness signal N 0 i se of the current frame.
  • the subband SN ratio calculating means 5 sets an unnecessary subband SN ratio. Since it is possible to prevent the S / N ratio from being reduced by performing the smoothing, it is possible to obtain an effect that it is possible to prevent the quality deterioration of the output voice.
  • the noise suppression device is suitable for a device that suppresses noise with characteristics with little fluctuation over the entire frequency band and reduces the generation of residual noise.

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  • Engineering & Computer Science (AREA)
  • Computational Linguistics (AREA)
  • Quality & Reliability (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Noise Elimination (AREA)
  • Cable Transmission Systems, Equalization Of Radio And Reduction Of Echo (AREA)

Abstract

La présente invention concerne un dispositif éliminateur de bruit comprenant un moyen de calcul de rapport signal/bruit de sous-bande permettant de calculer le spectre moyen du signal d'entrée de chaque sous-bande par réception d'un signal de vraisemblance de bruit, un spectre de signal d'entrée, et le spectre de bruit estimé de chaque sous-bande, de calculer le taux de mélange du spectre de bruit estimé et la moyenne du signal d'entrée de chaque sous-bande en fonction du signal de vraisemblance de bruit, et de calculer le rapport signal/bruit de chaque sous-bande en fonction du spectre de bruit estimé et de la moyenne du signal d'entrée de chaque sous-bande, et du taux de mélange.
PCT/JP2001/002596 2001-03-28 2001-03-28 Dispositif eliminateur de bruit WO2002080148A1 (fr)

Priority Applications (12)

Application Number Priority Date Filing Date Title
PCT/JP2001/002596 WO2002080148A1 (fr) 2001-03-28 2001-03-28 Dispositif eliminateur de bruit
JP2002578288A JP3574123B2 (ja) 2001-03-28 2001-03-28 雑音抑圧装置
CNB018101143A CN1282155C (zh) 2001-03-28 2001-03-28 噪声抑制装置和方法
EP10006260.3A EP2242049B1 (fr) 2001-03-28 2001-03-28 Dispositif de suppression du bruit
EP01917568A EP1376539B8 (fr) 2001-03-28 2001-03-28 Dispositif eliminateur de bruit
US10/276,292 US7349841B2 (en) 2001-03-28 2001-03-28 Noise suppression device including subband-based signal-to-noise ratio
EP10006261.1A EP2239733B1 (fr) 2001-03-28 2001-03-28 Procédé de suppression du bruit
DE60142800T DE60142800D1 (de) 2001-03-28 2001-03-28 Rauschunterdrücker
US11/927,354 US8412520B2 (en) 2001-03-28 2007-10-29 Noise reduction device and noise reduction method
US11/927,415 US7660714B2 (en) 2001-03-28 2007-10-29 Noise suppression device
US11/927,509 US20080056510A1 (en) 2001-03-28 2007-10-29 Noise suppression device
US11/927,478 US7788093B2 (en) 2001-03-28 2007-10-29 Noise suppression device

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/JP2001/002596 WO2002080148A1 (fr) 2001-03-28 2001-03-28 Dispositif eliminateur de bruit

Related Child Applications (7)

Application Number Title Priority Date Filing Date
EP10006261.1A Previously-Filed-Application EP2239733B1 (fr) 2001-03-28 2001-03-28 Procédé de suppression du bruit
US10276292 A-371-Of-International 2001-03-28
EP10006260.3A Previously-Filed-Application EP2242049B1 (fr) 2001-03-28 2001-03-28 Dispositif de suppression du bruit
US11/927,354 Continuation US8412520B2 (en) 2001-03-28 2007-10-29 Noise reduction device and noise reduction method
US11/927,478 Continuation US7788093B2 (en) 2001-03-28 2007-10-29 Noise suppression device
US11/927,415 Continuation US7660714B2 (en) 2001-03-28 2007-10-29 Noise suppression device
US11/927,509 Continuation US20080056510A1 (en) 2001-03-28 2007-10-29 Noise suppression device

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EP (3) EP2242049B1 (fr)
JP (1) JP3574123B2 (fr)
CN (1) CN1282155C (fr)
DE (1) DE60142800D1 (fr)
WO (1) WO2002080148A1 (fr)

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JP2006017940A (ja) * 2004-06-30 2006-01-19 Sony Corp 音響信号処理装置及び音声度合算出方法
JP4552533B2 (ja) * 2004-06-30 2010-09-29 ソニー株式会社 音響信号処理装置及び音声度合算出方法
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JPWO2007026691A1 (ja) * 2005-09-02 2009-03-26 日本電気株式会社 雑音抑圧の方法及び装置並びにコンピュータプログラム
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JPWO2009087923A1 (ja) * 2008-01-11 2011-05-26 日本電気株式会社 信号分析制御、信号分析、信号制御のシステム、装置、方法及びプログラム
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US8412520B2 (en) 2013-04-02
JPWO2002080148A1 (ja) 2004-07-22
EP2239733B1 (fr) 2019-08-21
EP1376539B8 (fr) 2010-12-15
US20080056509A1 (en) 2008-03-06
JP3574123B2 (ja) 2004-10-06
EP1376539A1 (fr) 2004-01-02
EP2242049A1 (fr) 2010-10-20
US7660714B2 (en) 2010-02-09
US7349841B2 (en) 2008-03-25
EP2239733A1 (fr) 2010-10-13
CN1282155C (zh) 2006-10-25
EP1376539B1 (fr) 2010-08-11
US20080059165A1 (en) 2008-03-06
US7788093B2 (en) 2010-08-31
US20040102967A1 (en) 2004-05-27
DE60142800D1 (de) 2010-09-23
EP2242049B1 (fr) 2019-08-07
EP1376539A4 (fr) 2007-04-18
CN1430778A (zh) 2003-07-16
US20080059164A1 (en) 2008-03-06
US20080056510A1 (en) 2008-03-06

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